Jun Li

Background: The Planetary Health Diet Index (PHDI) quantifies adherence to the planetary health diet, aiming to promote human health and environmental sustainability. While self-reported PHDI is linked to lower cardiometabolic risk, the underlying biological pathways remain unclear. We aimed to identify a metabolomic signature of PHDI and evaluate its association with cardiovascular disease (CVD) and mortality.Methods: In this prospective cohort study, we included 11,190 adults from three US cohorts (Nurses’ Health Study [NHS], NHS II, and Health Professionals Follow-up Study) with up to 30 years of follow-up. Self-reported PHDI and covariates, including body-mass index (BMI), sociodemographic, and lifestyle factors, were derived from the two questionnaires closest to blood draw. Plasma metabolites were profiled using liquid chromatography-tandem mass spectrometry. We used elastic net regression to identify a metabolomic signature of the PHDI. Associations with incident CVD (1,861 events) and mortality (3,788 deaths) were assessed using Cox proportional hazards models. Findings were replicated in the Women’s Health Initiative (WHI; n=1,711).Findings: We identified an 84-metabolite signature of PHDI (Pearson r=0·46), characterised by higher long-chain highly unsaturated lipids and plant-related amino acids and lower carnitines and saturated lipid species, reflecting favourable lipid remodelling and oxidative stress modulation. Higher metabolomic PHDI was associated with lower risks of CVD (hazard ratio [HR] per 1-SD increment 0·95 [95% CI 0·90-1·00]) and total mortality (0·93 [0·89-0·96]), independently of self-reported PHDI, BMI, and other conventional risk factors. The signature was replicated in WHI for CVD (0·92 [0·84-1·00]).Interpretation: We identified and validated a metabolomic signature of PHDI that captures biological pathways linking adherence to a sustainable dietary pattern with cardiovascular health. These findings provide mechanistic insight into the potential cardiometabolic benefits of the planetary health diet and identify metabolic pathways that could inform future prevention strategies.

Importance: Higher coffee intake has been associated with lower risk of type 2 diabetes (T2D), but the underlying biological pathways remain incompletely understood. Objective: To examine associations of coffee intake with insulin sensitivity, adiposity, and T2D risk, and assess whether coffee intake modifies associations between pathway-specific genetic susceptibility and incident T2D. Design, Setting, and Participants: Cross-sectional analyses among 806 participants without T2D in the VITamin D and OmegA-3 TriaL (VITAL) clinical sub-cohort, who underwent repeated dietary assessment, clinical phenotyping, and dual-energy X-ray absorptiometry imaging at baseline and year-2. Prospective analyses among 333,053 UK Biobank participants without T2D at baseline who had dietary and genetic data and were followed for a median of 13.3 years. Exposures: Coffee intake assessed by food frequency questionnaires. In UK Biobank, 12 pathway-specific polygenic scores (pPS) representing distinct T2D pathophysiological mechanisms were evaluated. Main Outcomes and Measures: The primary outcomes, in VITAL, were HbA1c, oral glucose tolerance test-derived measures of glucose response and insulin sensitivity, β-cell function, and overall, truncal, and visceral adiposity; in UK Biobank, was incident T2D. Results: In VITAL, higher coffee intake was associated with higher insulin sensitivity (standardized β per cup/day, 0.046; P = .004) and lower visceral adipose tissue mass (β, −0.047; P = .006), after adjusting for demographic, lifestyle, and clinical factors, including body mass index. In UK Biobank, higher coffee intake was associated with lower T2D incidence (hazard ratio per cup/day, 0.96; 95% CI, 0.95-0.97), lower triglyceride-to-HDL cholesterol ratio (β,−0.01; P = 2.51 × 10^-19), and lower visceral adipose tissue mass (β, −0.01; P = 4.28 × 10^-9). Associations of 3 pPS related to insulin resistance and fat distribution with incident T2D were attenuated among participants consuming higher amount of coffee than among non-consumers (P for interaction < .0043). Conclusions and Relevance: Higher coffee intake was associated with greater insulin sensitivity, lower visceral adiposity, and lower risk of T2D. Together with the attenuation of associations between pathway-specific genetic susceptibility and T2D risk among higher coffee consumers, these findings suggest that insulin resistance and visceral adiposity-related pathways may contribute to the association between coffee intake and T2D risk. Key Points: Question: Is coffee intake associated with specific insulin sensitivity and adiposity markers, and type 2 diabetes risk, and does it modify associations between pathway-specific genetic susceptibility and type 2 diabetes? Findings: In analyses repeated dietary, clinical, and imaging phenotyping in 806 VITAL participants and prospective data from 333,053 UK Biobank participants, higher coffee intake was associated with greater insulin sensitivity, lower visceral adiposity, and lower type 2 diabetes risk. Higher coffee intake also attenuated associations of three pathway-specific polygenic scores related to insulin resistance and fat distribution with type 2 diabetes risk. Meaning: These findings suggest that pathways related to insulin sensitivity and visceral adiposity may contribute to the associations between coffee intake and lower type 2 diabetes risk.

The human metabolome reflects complex metabolic states affected by genetic and environmental factors. However, metabolites associated with type 2 diabetes (T2D) risk and their determinants remain insufficiently characterized. Here we integrated blood metabolomic, genomic and lifestyle data from up to 23,634 initially T2D-free participants from ten cohorts. Of 469 metabolites examined, 235 were associated with incident T2D during up to 26 years of follow-up, including 67 associations not previously reported across bile acid, lipid, carnitine, urea cycle and arginine/proline, glycine and histidine pathways. Further genetic analyses linked these metabolites to signaling pathways and clinical traits central to T2D pathophysiology, including insulin resistance, glucose/insulin response, ectopic fat deposition, energy/lipid regulation and liver function. Lifestyle factors—particularly physical activity, obesity and diet—explained greater variations in T2D-associated versus non-associated metabolites, with specific metabolites revealed as potential mediators. Finally, a 44-metabolite signature improved T2D risk prediction beyond conventional factors. These findings provide a foundation for understanding T2D mechanisms and may inform precision prevention targeting specific metabolic pathways.

Circulating metabolites may reflect biological homeostasis and have been linked to dietary intakes and human health, and may hold the promises to facilitate objective assessments of intakes and metabolic response to diets. Here, we integrated metabolomic, genetic, and metagenomic data from five longitudinal cohorts comprising 21,474 participants of diverse ethnic backgrounds, to develop metabolomic signatures for popular dietary patterns (i.e., three guideline-based diets, three plant-based diets, and two mechanism-based diets) and systematically investigated their clinical relevance. Applying machine-learning models in two deeply-phenotyped lifestyle validation studies, we identified eight metabolomic signatures (each included 37 to 66 metabolites) significantly correlated with their respective dietary pattern indices, consistently across multiple independent validation cohorts (r = 0.11–0.38; P < 8.06×10⁻⁹). These signatures included shared metabolites between diets (e.g., up to 67% among guideline-based diets, including hippuric and 3-indolepropionic acid), and metabolites unique to specific diets (e.g., N6,N6,N6-trimethyllysine to proinflammatory diet). In multivariable-adjusted analyses of 5 prospective cohorts (1,832 incident cases during up to 27 years of follow-up), the metabolomic signatures of healthful diets (i.e., Mediterranean and healthful plant-based diets) were associated with lower T2D risk (HR: 0.82–0.90; P < 3×10⁻⁶), while signatures for unhealthy diets (e.g., proinflammatory and hyperinsulinemia diets) were associated with higher T2D risk (HR: 1.23–1.26; P < 2×10⁻¹⁵); these associations were further supported by Mendelian randomization analysis incorporating genetic data. Finally, through genome-wide and taxa-wide associating analyses, we identified 15 genetic loci – including those involved in fatty acid and energy metabolism (e.g., FADS1/2 and CERS4 ; P < 5×10 ^-8 ), and 39 gut microbial species – including those relevant to butyric acid metabolism (e.g., E. eligens and F. pranusnitzii ; FDR < 0.05), significantly associated with the metabolomic signatures of diets. Genetic variants and gut microbial diversity explained up to 19.1% and 10.6% of the variation in these signatures, respectively, underscoring a potential role of host genetics and gut microbiota in dietary metabolism. In conclusion, our study identified metabolomic signatures reflecting both intakes and individual metabolic response to various diets and are associated with future T2D risk. These signatures may facilitate individualized dietary assessments and risk stratification in future nutritional research.

OBJECTIVE: Body weight influences the blood metabolome, but whether specific plasma metabolomic profiles predict long-term weight change remains unclear. We aimed to investigate associations among plasma metabolites, current weight, weight change trajectories, and incident type 2 diabetes (T2D) in adults. RESEARCH DESIGN AND METHODS: We profiled 260 plasma metabolites in 7,499 U.S. health professionals. A metabolomic score predicting BMI change over time was derived using elastic net regression with a training/testing approach. Associations with incident T2D were examined using multivariable Cox models. RESULTS: Metabolomic predictors of BMI change varied by baseline weight status. In lean individuals, 46 metabolites significantly predicted BMI change slope compared with 20 individuals in the group with overweight and 13 in the group with obesity, with no overlapping metabolites across groups. A metabolomic score of BMI slope derived in lean participants correlated with BMI slope in the lean group (Spearman r = 0.31), but the correlation was weaker in the groups with overweight (r = 0.16) and obesity (r = 0.10). The score was associated with higher T2D risk in the lean group (hazard ratio [HR] per 1 SD 1.42; 95% CI 1.26–1.60) and group with overweight (HR 1.29; 95% CI 1.17–1.42) but not in the group with obesity. CONCLUSIONS: Plasma metabolomic profiles predictive of future weight change differ by body weight status. Distinct metabolic signatures may help identify certain lean individuals at an elevated risk of future weight gain and T2D, offering opportunities for earlier, more targeted prevention.

Type 2 innate lymphoid cells (ILC2s) regulate the proliferation of preadipocytes that give rise to beige adipocytes. Whether and how ILC2 downstream Th2 cytokines control beige adipogenesis remain unclear. We used cell systems and genetic models to examine the mechanism through which IL-13, an ILC2-derived Th2 cytokine, controls beige adipocyte differentiation. IL-13 priming in preadipocytes drove beige adipogenesis by upregulating beige-promoting metabolic programs, including mitochondrial oxidative metabolism and PPARγ-related pathways. The latter was mediated by increased expression and activity of PPARγ through the IL-13 receptor 1 (IL-13R1) downstream effectors STAT6 and p38 MAPK, respectively. Il13 -KO or preadipocyte Il13ra1 -KO mice were refractory to cold- or β3-adrenergic agonist–induced beiging in inguinal white adipose tissue, whereas Il4 -KO mice showed no defects in beige adipogenesis. Il13 -KO and Il13ra1 -KO mouse models exhibited increased body weight and fat mass and dysregulated glucose metabolism but had a mild cold-intolerant phenotype, likely due to their intact brown adipocyte recruitment. We also found that genetic variants of human IL13RA1 were associated with BMI and type 2 diabetes. These results suggest that IL-13 signaling–regulated beige adipocyte function may play a predominant role in modulating metabolic homeostasis rather than in thermoregulation.

As the global population ages, it is critical to identify diets that, beyond preventing noncommunicable diseases, optimally promote healthy aging. Here, using longitudinal questionnaire data from the Nurses’ Health Study (1986–2016) and the Health Professionals Follow-Up Study (1986–2016), we examined the association of long-term adherence to eight dietary patterns and ultraprocessed food consumption with healthy aging, as assessed according to measures of cognitive, physical and mental health, as well as living to 70 years of age free of chronic diseases. After up to 30 years of follow-up, 9,771 (9.3%) of 105,015 participants (66% women, mean age = 53 years (s.d. = 8)) achieved healthy aging. For each dietary pattern, higher adherence was associated with greater odds of healthy aging and its domains. The odds ratios for the highest quintile versus the lowest ranged from 1.45 (95% confidence interval (CI) = 1.35–1.57; healthful plant-based diet) to 1.86 (95% CI = 1.71–2.01; Alternative Healthy Eating Index). When the age threshold for healthy aging was shifted to 75 years, the Alternative Healthy Eating Index diet showed the strongest association with healthy aging, with an odds ratio of 2.24 (95% CI = 2.01–2.50). Higher intakes of fruits, vegetables, whole grains, unsaturated fats, nuts, legumes and low-fat dairy products were linked to greater odds of healthy aging, whereas higher intakes of trans fats, sodium, sugary beverages and red or processed meats (or both) were inversely associated. Our findings suggest that dietary patterns rich in plant-based foods, with moderate inclusion of healthy animal-based foods, may enhance overall healthy aging, guiding future dietary guidelines.

Background: Cognitive resilience (CR) refers to the continuum from worse to better‐than‐expected cognition, given the degree of neuropathology. Understanding mechanisms underlying CR could inform discovery of novel targets for dementia prevention; however, specific metabolic pathways underlying CR are yet to be elucidated.Methods: Our study included 484 deceased participants (mean age at death =91 years, 70.2% women) from the Religious Orders Study and Rush Memory and Aging Project. Metabolomics profiling was conducted at Metabolon Inc. by a chromatography–mass spectrometry in postmortem dorsolateral prefrontal cortex tissue, yielding 600 known metabolites quantified for analysis. Antemortem cognitive function was assessed annually by 17 neuropsychologic tests through death, and neuropathologies were evaluated in postmortem brain. CR was defined as the residual slope of cognitive decline after controlling for age at death, sex, education, and nine Alzheimer’s disease, other neurodegenerative, and cerebrovascular neuropathologies. We analyzed the associations between individual brain metabolites and CR score using linear regressions. We also constructed co‐regulatory networks for metabolites significantly related to CR, and identified key metabolites for CR within each network using recursive conditional analysis.Results: We identified 72 metabolites whose levels in the prefrontal cortex were associated with CR (FDR<0.05), encompassing diverse biochemical categories including 33 amino acids and peptides, 21 lipids, 7 cofactors and vitamins, 6 carbohydrates/energy metabolites, and 5 others. Based on partial correlations, the 72 CR‐related metabolites were constructed into 6 co‐regulatory networks, each linked to specific biological functions including oxidative stress (key metabolites driving the associations were N‐acetylmethionine sulfoxide, ergothioneine, and carotene diol), synaptic function (led by myo‐inositol, glycerate, and docosahexaenoyl ethanolamide), neuroinflammation (led by 7‐hydroxycholesterol), cell signaling (led by inositol 1‐phosphate and gamma‐glutamylthreonine), phospholipid metabolism (led by glycerophosphorylcholine), and energy/glucose metabolism (led by UDP‐glucose and adenosine monophosphate). The eigenvectors of all 6 co‐functional networks were significantly associated with CR (FDR<0.05).Conclusion: We identified brain metabolites implicated in oxidative stress and necroinflammation, synaptic function and cell signaling, and lipid/energy metabolism, associated with rates of cognitive decline after controlling for neuropathologies. These CR‐related metabolites may inform the discovery of novel targets for improving cognitive resilience to neuropathology.

INTRODUCTION: Dietary patterns are associated with dementia risk, but the underlying molecular mechanisms are largely unknown.METHODS: We used RNA sequencing data from post mortem prefrontal cortex tissue and annual cognitive evaluations from 1204 participants in the Religious Orders Study and Memory and Aging Project. We identified a transcriptomic profile correlated with the MIND diet (Mediterranean‐Dietary Approaches to Stop Hypertension Intervention for Neurodegenerative Delay) among 482 individuals who completed ante mortem food frequency questionnaires; and examined its associations with cognitive health in the remaining 722 participants.RESULTS: We identified a transcriptomic profile, consisting of 50 genes, correlated with the MIND diet score (p = 0.001). Each standard deviation increase in the transcriptomic profile score was associated with a slower annual rate of decline in global cognition (β = 0.011, p = 0.003) and lower odds of dementia (odds ratio = 0.76, p = 0.0002). Expressions of several genes (including TCIM and IGSF5) appeared to mediate the association between MIND diet and dementia.DISCUSSION: A brain transcriptomic profile for healthy diets revealed novel genes potentially associated with cognitive health.Highlights: Why healthy dietary patterns are associated with lower dementia risk are unknown. We integrated dietary, brain transcriptomic, and cognitive data in older adults. Mediterranean‐Dietary Approaches to Stop Hypertension Intervention for Neurodegenerative Delay (MIND) diet intake is correlated with a specific brain transcriptomic profile. This brain transcriptomic profile score is associated with better cognitive health. More data are needed to elucidate the causality and functionality of identified genes.

Type 2 diabetes (T2D) is known as one of the important risk factors for the severity and mortality of COVID-19. Here, we evaluate the impact of T2D and its genetic susceptibility on the severity and mortality of COVID-19, using 459,119 individuals in UK Biobank. Utilizing the polygenic risk scores (PRS) for T2D, we identified a significant association between T2D or T2D PRS, and COVID-19 severity. We further discovered the efficacy of vaccination and the pivotal role of T2D-related genetics in the pathogenesis of severe COVID-19. Moreover, we found that individuals with T2D or those in the high T2D PRS group had a significantly increased mortality rate. We also observed that the mortality rate for SARS-CoV-2-infected patients was approximately 2 to 7 times higher than for those not infected, depending on the time of infection. These findings emphasize the potential of T2D PRS in estimating the severity and mortality of COVID-19.